🤖 AI Summary
本文提出了一种近似均匀采样器，用于解决满足特定条件的约束满足问题，通过调用一种高效的近似计数算法，在多项式时间内生成接近均匀分布的解。
📝 Abstract
We give an approximately uniform sampler for satisfying assignments of constraint satisfaction problems that satisfy $4\mathrm e p(Δ+1)^2\le1$, where $p$ is the largest constraint-violation probability under the uniform product distribution, and $Δ$ is the maximum degree of the dependency graph. The algorithm invokes the recent efficient approximate counting algorithm of Liu, Wang, Yin, Zhang, and Zhou as a subroutine and returns a satisfying assignment sampled within total-variation distance $\varepsilon$ of the uniform distribution in $(n+m/\varepsilon)^{O(kΔ\log D)}$ time, where $n$ and $m$ are the numbers of variables and constraints, $D$ is the common domain size, and $k$ bounds the constraint arity.